Outlier absorbing based on a Bayesian approach
This addresses the issue of misleading results due to outliers in machine learning applications, but appears incremental as it builds on existing outlier handling approaches.
The paper tackles the problem of outliers in machine learning by proposing a new method that combines global and local views of samples to handle outliers robustly, with experimental results demonstrating its capabilities.
The presence of outliers is prevalent in machine learning applications and may produce misleading results. In this paper a new method for dealing with outliers and anomal samples is proposed. To overcome the outlier issue, the proposed method combines the global and local views of the samples. By combination of these views, our algorithm performs in a robust manner. The experimental results show the capabilities of the proposed method.